手法を比較

	多変量探索的量的研究	多変量相関研究
分野	研究デザイン	研究デザイン
系統	Process / pipeline	Process / pipeline
提唱年	1930s–1960s (foundational multivariate methods); codified in research design literature from the 1980s onward	1920s–1930s (multivariate extensions); consolidated in applied social science by 1970s
提唱者	Hair, Tabachnick, and colleagues (canonical synthesis); roots in Fisher, Hotelling, and Thurstone (early 20th century)	Developed from Galton and Pearson's bivariate correlation work, extended to multivariate contexts by R.A. Fisher, Harold Hotelling, and others
種類	Quantitative research design	Non-experimental quantitative research design
原典	Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540	Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541
別名	multivariate exploratory design, exploratory multivariate analysis, multivariate data exploration, MEQ research	multivariate correlational design, multivariate relational research, multiple-variable correlational study, multivariate associational research
関連	5	2
概要	Multivariate exploratory quantitative research is a design in which researchers simultaneously examine multiple quantitative variables without imposing a predetermined structural model, using techniques such as exploratory factor analysis, cluster analysis, or principal component analysis to detect latent patterns, natural groupings, or underlying dimensions in the data. The goal is discovery and pattern recognition rather than hypothesis confirmation.	Multivariate correlational research is a non-experimental quantitative design that examines the simultaneous associations among three or more variables. Rather than manipulating conditions, the researcher measures naturally occurring variables and uses techniques such as multiple regression, canonical correlation, or structural equation modeling to map the pattern and strength of their interrelationships. It is the dominant design when the goal is to understand how a set of predictors jointly relates to one or more outcome variables.